Principal Engineer Tech Lead, Embodied AI & Off-Board Performance Evaluation

Role Description

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, verifying and validating the autonomy stack, and defining, measuring, and validating system performance targets.

We are seeking a talented Principal Engineer to technically oversee the design, development, and deployment of a novel Embodied AI and Large Language Model (LLM)-based monitoring framework for off-board scenario understanding, intelligence evaluation, and root cause analysis.

You will lead the creation of a specialized, off-board evaluation layer that consumes AV drive logs and fleet data to automatically infer scenario safety and assess driving intelligence without running live on the vehicle.

What You'll Be Doing:

• Embodied AI & LLM Framework Oversight: Technically oversee the architecture to identify, describe, and enrich events in historical vehicle logs using Multimodal LLMs.
• Multi-Modal Data Fusion: Oversee the off-board ingestion and fusion of semantic scene descriptions, ego-centric kinematics, and internal autonomy telemetry to create a holistic diagnostic context for LLM inference.
• Prompt Engineering & Model Integration: Develop structured prompting templates utilizing Contextual Prompting (CP), Chain-of-Thought (CoT), and In-Context Learning (ICL) to evaluate scenarios.
• AI Inference & Evaluation Scalability: Architect the integration of foundation models into the Metrics Engine (ME), designing efficient cascade filtering and log slice parallelization strategies to scale high-volume LLM inference across simulation and on-road drive logs while managing computational latency and costs.
• AV Performance Metrics Foundations: Define, design, and implement key metrics to evaluate autonomous vehicle performance, such as lane change capability, oscillations, and braking.
• Driving Policy Integration: Deploy and manage a Retrieval-Augmented Generation (RAG) vector database containing codified AV Driving Policies to ground off-board LLM evaluations in specific Operational Design Domains.
• Cross-Functional Technical Leadership: Serve as a technical escalation point and collaborate with Autonomy (Planner, Prediction, Perception) and Systems teams to deliver high-signal, enriched events.
• Advanced Physical AI R&D: Drive the transition toward Direct Vector-LLM Fusion utilizing emerging Physical AI ecosystems and open-weights Vision-Language-Action (VLA) models to process telemetry off-board without text-translation bottlenecks.

Qualifications

• 10+ years of professional experience in software engineering, applied AI/ML, or autonomous vehicle systems development.
• Bachelor's degree in Computer Science, Engineering, Robotics, or a related field.
• Proven experience working with Large Language Models (LLMs) and Vision-Language Models (VLMs) for reasoning, parsing, and scene description.
• Experience with parameter-efficient fine-tuning and deploying open-weights models on internal infrastructure.
• Familiarity with local and cloud vector databases, such as LanceDB, for housing output vector embeddings.
• Experience with adversarial scenario generation and closed-loop simulation environments.
• Strong background leveraging software to develop frameworks, libraries, and tools for calculating and aggregating AV performance metrics.
• Strong analytical and problem-solving skills, particularly in the context of complex system performance evaluation.
• Expert-level proficiency in Python and strong understanding of software development principles.

Bonus Points (not required)

• Experience working with autonomous vehicle sensor data, including its processing and integration.
• Hands-on experience with data pipeline orchestration tools and distributed data processing frameworks.
• Expertise managing cloud infrastructure on AWS or GCP for processing terabytes of data efficiently.
• Familiarity with Ray and Ray clusters for scaling Python applications and AI/ML tasks.
• Expertise with C++ programming for data frameworks.

Success Metrics

• Successful deployment of an automated off-board evaluator capable of inferring scenario safety and assessing AV intelligence with a target >90% F1 score for anomaly detection.
• Successful delivery of high-signal, LLM-enriched events and robust performance metrics that enable Autonomy teams to efficiently diagnose and resolve complex, long-tail behavioral issues.
• Reduction of the turnaround time required to move from fleet data collection to actionable simulation scenario creation from days to hours.
• Improvement in the efficiency and effectiveness of AV performance evaluation processes.

Physical Demands

• Frequently required to sit, talk, or hear.
• Occasionally required to stand and walk; use hands to finger, handle, or feel; reach with hands and arms.
• Must occasionally lift and move up to 50 pounds.

Travel Requirements

• Expected to travel for 1-2 weeks each quarter visiting all of our testing sites - mostly to LV, but also domestically as needed for the business.

Working Environment

• Regularly exposed to mechanical and computer parts.
• Occasionally exposed to fumes and/or airborne particles.
• The noise level in the environment is low to moderate.

Salary Range

$200,000 — $275,000 USD

Benefits

• Medical, dental, vision.
• 401k with a company match.
• Health saving accounts.
• Life insurance.
• Pet insurance.
• And more.